Hybrid Cat-Particle Swarm Optimization Algorithm on Bounded Knapsack Problem with Multiple Constraints

DOI

10.2991/acsr.k.220202.045How to use a DOI?

Keywords

Hybrid cat-particle swarm optimization; Metaheuristic; Modified bounded knapsack problem

Abstract

Optimization problems have become interesting problems to discuss, including the knapsack problem. There are many types and variations of knapsack problems. In this paper, the authors introduce a new hybrid metaheuristic algorithm to solve the modified bounded knapsack problem with multiple constraints we call it modified bounded knapsack problem with multiple constraints (MBKP-MC). Authors combine two popular metaheuristic algorithms, Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The algorithm is named Hybrid Cat-Particle Swarm Optimization (HCPSO). The results of the implementation of the algorithm are compared with PSO and CSO algorithms. Based on the experimental results, it is known that the HCPSO algorithm is suitable and can reach to good-quality solution within a reasonable computation time. In addition, the new proposed algorithm performs better than the PSO and CSO on all MBKP-MC data used.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Kiswara Agung Santoso
AU  - Muhammad Bagus Kurniawan
AU  - Ahmad Kamsyakawuni
AU  - Abduh Riski
PY  - 2022
DA  - 2022/02/08
TI  - Hybrid Cat-Particle Swarm Optimization Algorithm on Bounded Knapsack Problem with Multiple Constraints
BT  - Proceedings of the  International Conference on Mathematics, Geometry, Statistics, and Computation (IC-MaGeStiC 2021)
PB  - Atlantis Press
SP  - 244
EP  - 248
SN  - 2352-538X
UR  - https://doi.org/10.2991/acsr.k.220202.045
DO  - 10.2991/acsr.k.220202.045
ID  - Santoso2022
ER  -